Comparison between Model-Based Q-Learning Vs. Model-Free Q-Learning
Rasel MahmudA comparison table between Model-Based Q-Learning and Model-Free Q-Learning:
| Aspect | Model-Based Q-Learning | Model-Free Q-Learning |
| Definition | Uses a model of the environment (transition and reward functions) to make decisions. | Learns directly from interactions with the environment, without using a model. |
| Environment Knowledge | Requires a model of the environment, which includes the transition probabilities and reward functions. | Does not require a model. It learns from trial and error, interacting with the environment. |
| Learning Process | Learns a model (transition and reward functions) first, then plans using the model to select actions. | Directly learns Q-values through trial-and-error interaction with the environment. |
| Exploration | Can use the model to simulate actions and explore potential states before actually taking actions. | Requires exploration (typically through epsilon-greedy or other policies) to update Q-values. |
| Efficiency | More efficient if the model is accurate, as planning with a model can reduce the number of real-world interactions needed. | Typically requires more interactions with the environment to converge to optimal policies. |
| Real-World Interaction | Fewer real-world interactions may be required after learning the model, as the agent can simulate outcomes. | Requires many real-world interactions for learning, as it only uses actual experiences. |
| Memory Requirements | Requires storing the model of the environment (transition probabilities, reward functions). | Requires storing Q-values for each state-action pair (less complex in terms of memory). |
| Computation Cost | Potentially high due to the need to maintain and update the model, especially for complex environments. | Generally has lower computation cost, as it only needs to update Q-values based on observed rewards. |
| Accuracy | Accuracy depends on the quality of the learned model (if the model is inaccurate, the agent may make suboptimal decisions). | The agent directly learns from experience, so the accuracy depends on sufficient exploration and sufficient learning over time. |
| Adaptability | Can adapt quickly if the model is able to capture changes in the environment (model can be updated). | May take longer to adapt, as learning is based solely on the observed interactions with the environment. |
| Example Algorithms | Dyna-Q, Monte Carlo Tree Search (MCTS), Value Iteration, Dynamic Programming. | Standard Q-Learning, SARSA, Deep Q-Network (DQN), Double Q-Learning. |
| Suitability for Complex Environments | Better suited for environments where the transition dynamics and rewards are difficult to learn purely from experience (e.g., planning in a simulated environment). | More suitable for environments where the model is unknown and can only be learned from interaction, such as large state-action spaces or real-time applications. |
Key Differences:
Model-Based Q-Learning requires a model of the environment (transition and reward functions) and can plan based on that model. It tries to predict outcomes and optimize decisions before actually interacting with the environment.
Model-Free Q-Learning directly learns from interactions with the environment. It updates its Q-values through experience without relying on a pre-defined model of the environment.
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Written by
Rasel Mahmud
Rasel Mahmud
I am currently pursuing a Master’s in Software Engineering at USTC, with a strong focus on Machine Learning for AI-driven solutions. My journey has been diverse, in addition to my academic journey. In 2020, I started to learn graphic design➡️video editing➡️digital marketing and later dived into➡️Full-Stack JavaScript web development. Each experience has strengthened my ability to analyze, optimize, and create impactful solutions. From 2023, I'm training myself in machine learning end-to-end project approach to solving real-world challenges.